US Army Combat Capabilities Development Command had been doing research on robotics for over a decade. But it has only recently that military personnel have begun to integrate this technology into their battlefield. They have been testing their robots, and using them to scan for IEDs and recover Ravens. The US Marine Corps is also testing a recon robot for urban warfare. These robots work together with Soldiers. These robots can execute tactical actions according to plan, and can suggest changes to plans.
The US Army has developed an algorithm-based teaching system that will allow Soldier robotics to communicate with each others. This system will allow robots and Soldiers to communicate using spoken language. The system will allow the robots ask questions to overcome doubt. This will enable robots perform joint tasks at operational speeds, and it will also reduce the learning curves for Soldiers.
The most difficult part of creating an algorithm-based learning system was figuring out what questions to ask. The natural language understanding pipeline was developed by the research team. It processes Soldier's spoken words and is based on statistical classification methods. The system was trained on a small number of humanrobot dialog. The research team was able to reduce variability in natural languages. The team is currently working on connecting the output semantic representation of a Soldier's spoken language to the grounding system of the robot.
Algorithm-based Learning System aims to improve Soldier-robot collaboration in tactical environments. This will allow the robots and Soldiers to communicate using natural language. A natural language processing pipeline was developed by the research team that interprets Soldiers' spoken language. The system will eventually be able to tame the variation in spoken language. This will give the robots the ability to act on the intent of language, which will enable them to become more collaborative.
The algorithm-based teaching system will allow robots to ask Soldiers for information to better understand their environment. The questions will use information from maps. These maps will help drive autonomous robot-based decisions. The questions are also tactically relevant. These questions are focused on how robots will make decisions and what information Soldiers require. Soldiers were asked to provide feedback. The Soldiers' feedback was used to ensure that robots could recognize maps that were relevant for tactical decisions.
The Soldier-bot critical variant has a faster attack rate, and fires critical rockets faster than other Soldier-bot variants. These rockets can do six times more damage than regular rockets. The critical variant can also deal more damage and can be used to kill all players, even without crit resistance.